Academic literature on the topic 'VADER Sentiment Analysis'

The advent of the Internet and its social media platforms have affected people’s daily life. More and more people use it as a tool in order to communicate, exchange opin-ions and share information with others. However, those platforms have not only been used for socializing but also for expressing people’s product preferences. This wide spread of social networking sites has enabled companies to take advantage of them as an important way of approaching their target audience. This thesis focuses on study-ing the influence of social media platforms on the revenue of a single organization like Nike that uses them actively. Facebook and Twitter, two widely-used social me-dia platforms, were investigated with tweets and comments produced by consumer’s online discussions in brand’s hosted pages being gathered. This unstructured social media data were collected from 26 Nike official pages, 13 fan pages from each plat-form and their sentiment was analyzed. The classification of those comments had been done by using the Valence Aware Dictionary and Sentiment Reasoner (VADER), a lexicon-based approach that is implemented for social media analysis. After gathering the five-year Nike’s revenue, the degree to which these could be affected by the clas-sified data was examined by using multiple stepwise linear regression analysis. The findings showed that the fraction of positive/total for both Facebook and Twitter ex-plained 84.6% of the revenue’s variance. Fitting this data on the multiple regression model, Nike’s revenue could be forecast with a root mean square error around 287 billion.

Instagram is one of the main social platforms for business promotion. Millions of potential customers and endless visual marketing opportunities makes Instagram a perfect place to increase online sales. There are many tools and mechanisms to promote brands on Instagram such as paid advertising or using a pre-generated set of popular hashtags. In this regard, the presence and content of users’ comments becomes an important socio-psychological factor in the motivation to buy or use a product or service. The goal of this degree project is to investigate natural language processing techniques applied to users’ comments on Instagram in order to determine a new algorithm that will include content analysis to the list of feed ranking factors. As it is now, the user has to read through posts on Instagram to get an idea of the quality of a product or service. Therefore, a way to classify and rank products and services is needed. We propose a new algorithm called "Wed 2.0" that can assist consumers in their search of wedding services and products on Instagram. Data mining techniques and sentiment analysis are used to define the mood of the comments and structure user opinions as well as to rank accounts based on this knowledge.

Background Daily, companies generate enormous amounts of customer support tickets which are grouped and placed in specialised queues, based on some characteristics, from where they are resolved by the customer support personnel (CSP) on a first-in-first-out basis. Given that these tickets require different levels of urgency, a logical next step to improving the effectiveness of the CSPs is to prioritise the tickets based on business policies. Among the several heuristics that can be used in prioritising tickets is sentiment polarity. Objectives This study investigates how machine learning methods and natural language techniques can be leveraged to automatically predict the sentiment polarity of customer support tickets using. Methods Using a formal experiment, the study examines how well Support Vector Machine (SVM), Naive Bayes (NB) and Logistic Regression (LR) based sentiment polarity prediction models built for the product and movie reviews, can be used to make sentiment predictions on email support tickets. Due to the limited size of annotated email support tickets, Valence Aware Dictionary and sEntiment Reasoner (VADER) and cluster ensemble - using k-means, affinity propagation and spectral clustering, is investigated for making sentiment polarity prediction. Results Compared to NB and LR, SVM performs better, scoring an average f1-score of .71 whereas NB scores least with a .62 f1-score. SVM, combined with the presence vector, outperformed the frequency and TF-IDF vectors with an f1-score of .73 while NB records an f1-score of .63. Given an average f1-score of .23, the models transferred from the movie and product reviews performed inadequately even when compared with a dummy classifier with an f1-score average of .55. Finally, the cluster ensemble method outperformed VADER with an f1-score of .61 and .53 respectively. Conclusions Given the results, SVM, combined with a presence vector of bigrams and trigrams is a candidate solution for extracting sentiments from email support tickets. Additionally, transferring sentiment models from the movie and product reviews domain to the email support tickets is not possible. Finally, given that there exists a limited dataset for conducting sentiment analysis studies in the Swedish and the customer support context, a cluster ensemble is recommended as a sample selection method for generating annotated data.

Traditional methods used to investigate the usage of parks consists of questionnaire which is both a very time- and- resource consuming method. Today more than four billion people daily use some form of social media platform. This has led to the creation of huge amount of data being generated every day through various social media platforms and has created a potential new source for retrieving large amounts of data. This report will investigate a modern approach, using Natural Language Processing on Twitter data to understand how parks in Stockholm being used. Natural Language Processing (NLP) is an area within artificial intelligence and is referred to the process to read, analyze, and understand large amount of text data and is considered to be the future for understanding unstructured text. Twitter data were obtained through Twitters open API. Data from three parks in Stockholm were collected between the periods 2015-2019. Three analysis were then performed, temporal, sentiment, and topic modeling analysis. The results from the above analysis show that it is possible to understand what attitudes and activities are associated with visiting parks using NLP on social media data. It is clear that sentiment analysis is a difficult task for computers to solve and it is still in an early stage of development. The results from the sentiment analysis indicate some uncertainties. To achieve more reliable results, the analysis would consist of much more data, more thorough cleaning methods and be based on English tweets. One significant conclusion given the results is that people’s attitudes and activities linked to each park are clearly correlated with the different attributes each park consists of. Another clear pattern is that the usage of parks significantly peaks during holiday celebrations and positive sentiments are the most strongly linked emotion with park visits. Findings suggest future studies to focus on combining the approach in this report with geospatial data based on a social media platform were users share their geolocation to a greater extent.<br>Traditionella metoder använda för att förstå hur människor använder parker består av frågeformulär, en mycket tids -och- resurskrävande metod. Idag använder mer en fyra miljarder människor någon form av social medieplattform dagligen. Det har inneburit att enorma datamängder genereras dagligen via olika sociala media plattformar och har skapat potential för en ny källa att erhålla stora mängder data. Denna undersöker ett modernt tillvägagångssätt, genom användandet av Natural Language Processing av Twitter data för att förstå hur parker i Stockholm används. Natural Language Processing (NLP) är ett område inom artificiell intelligens och syftar till processen att läsa, analysera och förstå stora mängder textdata och anses vara framtiden för att förstå ostrukturerad text. Data från Twitter inhämtades via Twitters öppna API. Data från tre parker i Stockholm erhölls mellan perioden 2015–2019. Tre analyser genomfördes därefter, temporal, sentiment och topic modeling. Resultaten från ovanstående analyser visar att det är möjligt att förstå vilka attityder och aktiviteter som är associerade med att besöka parker genom användandet av NLP baserat på data från sociala medier. Det är tydligt att sentiment analys är ett svårt problem för datorer att lösa och är fortfarande i ett tidigt skede i utvecklingen. Resultaten från sentiment analysen indikerar några osäkerheter. För att uppnå mer tillförlitliga resultat skulle analysen bestått av mycket mer data, mer exakta metoder för data rensning samt baserats på tweets skrivna på engelska. En tydlig slutsats från resultaten är att människors attityder och aktiviteter kopplade till varje park är tydligt korrelerat med de olika attributen respektive park består av. Ytterligare ett tydligt mönster är att användandet av parker är som högst under högtider och att positiva känslor är starkast kopplat till park-besök. Resultaten föreslår att framtida studier fokuserar på att kombinera metoden i denna rapport med geospatial data baserat på en social medieplattform där användare delar sin platsinfo i större utsträckning.

The rapid growth of social media in the last decades led e-commerce into a new era of value co-creation between the seller and the consumer. Since there is no contact with the product, people have to rely on the description of the seller, knowing that sometimes it may be biased and not entirely truth. Therefore, reviewing systems emerged in order to provide more trustworthy sources of information, since customer opinions may be less biased. The problem was, once sellers realized the importance of reviews and their direct impact on sales, the need to control this key factor arose. One of the methods developed was to offer customers a certain product in exchange for an honest review. However, in the light of the results of some studies, these "honest" reviews were proved to be biased and skew the overall rating of the product. The purpose of this work is to find patterns in these incentivized reviews and create a model that may predict whether a new review is biased or not. To study this subject, besides the sentiment analysis performed on the data, some other characteristics were taken into account, such as the overall rating, helpfulness rate, review length and the timestamp when the review was written. Results show that some of the most significant characteristics when predicting an incentivized review are the length of a review, its helpfulness rate and the overall polarity score, calculated through VADER algorithm, as the most important sentiment-related factor.<br>O rápido crescimento das redes sociais nas últimas décadas levaram o comércio electrónico a uma nova era de co-criação de valor entre o vendedor e o consumidor. Uma vez que não há contacto com o produto, os clientes têm de se basear na descrição do vendedor, mesmo sabendo que por vezes tal descrição pode ser tendenciosa e não totalmente verdadeira. Deste modo, surgiu um sistema de reviews com o propósito de disponibilizar um meio de informação de maior confiança, uma vez que se trata de partilha de informação entre clientes e por isso mais imparcial. No entanto, quando os vendedores se aperceberam da importância das "reviews" e o seu impacto direto nas vendas, surgiu a necessidade de controlar este fator chave. Uma das formas de o fazer foi através da oferta de determinados produtos em troca de "reviews" honestas. Contudo, à luz dos resultados de alguns estudos, foi demonstrado que estas "reviews" "honestas" são tendenciosas e enviesam a classificação geral do produto. O objetivo deste estudo foi o de encontrar padrões na forma como estas "reviews" incentivadas são escritas e criar um modelo para prever se uma determinada review seria enviesada. Para esta análise, além da análise de sentimentos realizada sobre os dados, outras características foram tidas em conta, tal como a classificação geral, a taxa de "helpfulness", o tamanho da "review" e a hora a que foi escrita. Os modelos gerados mostraram que as características mais importantes na previsão de parcialidade numa "review" são o tamanho e a taxa de utilidade e como característica sentimental mais relevante a pontuação geral da "review", calculada através do algoritmo VADER.